ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Sustainable and Intelligent Phytoprotection
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1617997
ALPD-Net: A wild licorice detection network based on UAV imagery
Provisionally accepted
- Shihezi University, Shihezi, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Licorice has significant medicinal and ecological importance. However, prolonged overharvesting has resulted in twofold damage to wild licorice resources and the ecological environment. Thus, precisely determining the distribution and growth condition of wild licorice is critical. Traditional licorice resource survey methods are unsuitable for complex terrain and do not meet the requirements of large-scale monitoring.In order to solve this problem, this study constructs a new dataset of wild licorice that was gathered using Unmanned Aerial Vehicle (UAV) and proposes a novel detection network named ALPD-Net for identifying wild licorice. To improve the model's performance in complex backgrounds, an Adaptive Background Suppression Module (ABSM) was designed.Through adaptive channel space and positional encoding, background interference is effectively suppressed. Additionally, to enhance the model's attention to licorice at different scales, a Lightweight Multi-Scale Module (LMSM) using multi-scale dilated convolution is introduced, significantly reducing the probability of missed detections. At the same time, a Progressive Feature Fusion Module (PFFM) is developed, where a weighted self-attention fusion strategy is employed to effectively merge detailed and semantic information from adjacent layers, thereby preventing information loss or mismatches.The experimental results show that ALPD-Net achieves good detection accuracy in wild licorice identification, with precision 73.3%, recall 76.1%, and mean Average Precision at IoU=0.50 (mAP50) of 79.5%. Further comparisons with mainstream object detection models show that ALPD-Net not only provides higher detection accuracy for wild licorice, but also dramatically reduces missed and false detections. These features make ALPD-Net a potential option for large-scale surveys and monitoring of wild licorice resources using UAV remote sensing.
Keywords: UAV imagery, Licorice detection, Background suppression, Feature fusion, deep learning
Received: 25 Apr 2025; Accepted: 23 Jun 2025.
Copyright: © 2025 Yang, Qin, Dai, Zhang, Xu, Qin and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Huaibin Qin, Shihezi University, Shihezi, China
Jianguo Dai, Shihezi University, Shihezi, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.